ML Price Prediction for 100,000 UK Used Cars - Visualization.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "09e7ff51",
   "metadata": {
    "toc": true
   },
   "source": [
    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"><li><ul class=\"toc-item\"><li><span><a href=\"#بسم-الله-الرحمن-الرحيم\" data-toc-modified-id=\"بسم-الله-الرحمن-الرحيم-0.1\"><strong>بسم الله الرحمن الرحيم</strong></a></span></li><li><span><a href=\"#“In-the-Name-of-Allah,-the-Most-Beneficent,-the-Most-Merciful”\" data-toc-modified-id=\"“In-the-Name-of-Allah,-the-Most-Beneficent,-the-Most-Merciful”-0.2\">“In the Name of Allah, the Most Beneficent, the Most Merciful”</a></span></li></ul></li><li><span><a href=\"#Introduction:-Machine-Learning-Project-(Price-Prediction-for-100,000-UK-Used-Cars)\" data-toc-modified-id=\"Introduction:-Machine-Learning-Project-(Price-Prediction-for-100,000-UK-Used-Cars)-1\">Introduction: Machine Learning Project (Price Prediction for 100,000 UK Used Cars)</a></span><ul class=\"toc-item\"><li><span><a href=\"#Content-of-data\" data-toc-modified-id=\"Content-of-data-1.1\">Content of data</a></span></li><li><span><a href=\"#Machine-Learning-Workflow\" data-toc-modified-id=\"Machine-Learning-Workflow-1.2\">Machine Learning Workflow</a></span></li><li><span><a href=\"#Imports\" data-toc-modified-id=\"Imports-1.3\">Imports</a></span></li></ul></li><li><span><a href=\"#Data-Cleaning-and-Formatting\" data-toc-modified-id=\"Data-Cleaning-and-Formatting-2\">Data Cleaning and Formatting</a></span><ul class=\"toc-item\"><li><span><a href=\"#Load-in-the-Data-and-Examine\" data-toc-modified-id=\"Load-in-the-Data-and-Examine-2.1\">Load in the Data and Examine</a></span><ul class=\"toc-item\"><li><span><a href=\"#I-found-that:\" data-toc-modified-id=\"I-found-that:-2.1.1\">I found that:</a></span><ul class=\"toc-item\"><li><span><a href=\"#2)-these-csv-files-are-missing-tax-column-(cclass.csv,-focus.csv)\" data-toc-modified-id=\"2)-these-csv-files-are-missing-tax-column-(cclass.csv,-focus.csv)-2.1.1.1\">2) these csv files are missing tax column (cclass.csv, focus.csv)</a></span></li><li><span><a href=\"#3)-these-csv-files-are-missing-mpg-column-(cclass.csv,-focus.csv)\" data-toc-modified-id=\"3)-these-csv-files-are-missing-mpg-column-(cclass.csv,-focus.csv)-2.1.1.2\">3) these csv files are missing mpg column (cclass.csv, focus.csv)</a></span></li><li><span><a href=\"#4)-hyundi.csv-file-have-a-column-named-tax(£)-which-should-be-tax.\" data-toc-modified-id=\"4)-hyundi.csv-file-have-a-column-named-tax(£)-which-should-be-tax.-2.1.1.3\">4) hyundi.csv file have a column named tax(£) which should be tax.</a></span></li><li><span><a href=\"#5)-the-two-unclead-files-have-a-different-columns-than-the-others,-so-i-have-to-deal-with-both-of-them-separately.\" data-toc-modified-id=\"5)-the-two-unclead-files-have-a-different-columns-than-the-others,-so-i-have-to-deal-with-both-of-them-separately.-2.1.1.4\">5) the two unclead files have a different columns than the others, so i have to deal with both of them separately.</a></span></li><li><span><a href=\"#6)-I-need-to-add-company-column-which-contains-company-name-for-all-files\" data-toc-modified-id=\"6)-I-need-to-add-company-column-which-contains-company-name-for-all-files-2.1.1.5\">6) I need to add company column which contains company name for all files</a></span></li></ul></li><li><span><a href=\"#From-the-results-above-i-found-that:\" data-toc-modified-id=\"From-the-results-above-i-found-that:-2.1.2\">From the results above i found that:</a></span><ul class=\"toc-item\"><li><span><a href=\"#These-columns-are-ok--[Company-name,-fuel-type2,-model,-year,-transmission]\" data-toc-modified-id=\"These-columns-are-ok--[Company-name,-fuel-type2,-model,-year,-transmission]-2.1.2.1\">These columns are ok  [Company name, fuel type2, model, year, transmission]</a></span></li></ul></li></ul></li><li><span><a href=\"#Missing-Values\" data-toc-modified-id=\"Missing-Values-2.2\">Missing Values</a></span></li></ul></li><li><span><a href=\"#Exploratory-Data-Analysis--(EDA)\" data-toc-modified-id=\"Exploratory-Data-Analysis--(EDA)-3\">Exploratory Data Analysis  (EDA)</a></span><ul class=\"toc-item\"><li><span><a href=\"#Data-cleaning\" data-toc-modified-id=\"Data-cleaning-3.1\">Data cleaning</a></span></li><li><span><a href=\"#Visualization\" data-toc-modified-id=\"Visualization-3.2\">Visualization</a></span></li></ul></li></ul></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6daa8a0",
   "metadata": {},
   "source": [
    "## **بسم الله الرحمن الرحيم**\n",
    "## “In the Name of Allah, the Most Beneficent, the Most Merciful”"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "866a47a3",
   "metadata": {},
   "source": [
    "# Introduction: Machine Learning Project (Price Prediction for 100,000 UK Used Cars)\n",
    "\n",
    "In this notebook, I will walk through solving a complete machine learning problem using a real-world dataset. \n",
    "\n",
    "This is a supervised, regression machine learning task: given a set of data with targets (in this case the price).\n",
    "\n",
    "* Supervised problem: we are given both the features and the target\n",
    "* Regression problem: the target is a continous variable\n",
    "\n",
    "## Content of data\n",
    "* Scraped data of used cars listings. 100,000 listings, which have been separated into files corresponding to each car manufacturer (total 13 csv files).\n",
    "\n",
    "* The cleaned data set contains information of price, transmission, mileage, fuel type, road tax, miles per gallon (mpg), and engine size (in liters).\n",
    "\n",
    "\n",
    "## Machine Learning Workflow\n",
    "\n",
    "1. Data cleaning and formatting\n",
    "2. Exploratory data analysis\n",
    "3. Feature engineering and selection\n",
    "4. Establish a baseline and compare several machine learning models on a performance metric\n",
    "5. Perform hyperparameter tuning on the best model to optimize it for the problem\n",
    "6. Evaluate the best model on the testing set\n",
    "7. Interpret the model results to the extent possible\n",
    "8. Draw conclusions and write a well-documented report\n",
    "\n",
    "let's get started! "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab3ed69d",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "25a71009",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import glob\n",
    "import datasist as ds\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline\n",
    "sns.set(rc={'figure.figsize': [14, 10]}, font_scale=1.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02d96b33",
   "metadata": {},
   "source": [
    "# Data Cleaning and Formatting\n",
    "\n",
    "## Load in the Data and Examine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0d820d8c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['audi.csv',\n",
       " 'bmw.csv',\n",
       " 'cclass.csv',\n",
       " 'focus.csv',\n",
       " 'ford.csv',\n",
       " 'hyundi.csv',\n",
       " 'merc.csv',\n",
       " 'skoda.csv',\n",
       " 'toyota.csv',\n",
       " 'unclean cclass.csv',\n",
       " 'unclean focus.csv',\n",
       " 'vauxhall.csv',\n",
       " 'vw.csv']"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the names of all csv files in the data set\n",
    "\n",
    "all_csv_files = glob.glob(\"*.csv\")\n",
    "all_csv_files"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01b0bd6a",
   "metadata": {},
   "source": [
    "> **I found that there is 2 uncleaned data files named (unclean cclass.csv, unclean focus.csv)  so i have to deal with them separately**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b63271a5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "audi.csv\n",
      "----------\n",
      "Index(['model', 'year', 'price', 'transmission', 'mileage', 'fuelType', 'tax',\n",
      "       'mpg', 'engineSize'],\n",
      "      dtype='object')\n",
      "======================================================================\n",
      "bmw.csv\n",
      "----------\n",
      "Index(['model', 'year', 'price', 'transmission', 'mileage', 'fuelType', 'tax',\n",
      "       'mpg', 'engineSize'],\n",
      "      dtype='object')\n",
      "======================================================================\n",
      "cclass.csv\n",
      "----------\n",
      "Index(['model', 'year', 'price', 'transmission', 'mileage', 'fuelType',\n",
      "       'engineSize'],\n",
      "      dtype='object')\n",
      "======================================================================\n",
      "focus.csv\n",
      "----------\n",
      "Index(['model', 'year', 'price', 'transmission', 'mileage', 'fuelType',\n",
      "       'engineSize'],\n",
      "      dtype='object')\n",
      "======================================================================\n",
      "ford.csv\n",
      "----------\n",
      "Index(['model', 'year', 'price', 'transmission', 'mileage', 'fuelType', 'tax',\n",
      "       'mpg', 'engineSize'],\n",
      "      dtype='object')\n",
      "======================================================================\n",
      "hyundi.csv\n",
      "----------\n",
      "Index(['model', 'year', 'price', 'transmission', 'mileage', 'fuelType',\n",
      "       'tax(£)', 'mpg', 'engineSize'],\n",
      "      dtype='object')\n",
      "======================================================================\n",
      "merc.csv\n",
      "----------\n",
      "Index(['model', 'year', 'price', 'transmission', 'mileage', 'fuelType', 'tax',\n",
      "       'mpg', 'engineSize'],\n",
      "      dtype='object')\n",
      "======================================================================\n",
      "skoda.csv\n",
      "----------\n",
      "Index(['model', 'year', 'price', 'transmission', 'mileage', 'fuelType', 'tax',\n",
      "       'mpg', 'engineSize'],\n",
      "      dtype='object')\n",
      "======================================================================\n",
      "toyota.csv\n",
      "----------\n",
      "Index(['model', 'year', 'price', 'transmission', 'mileage', 'fuelType', 'tax',\n",
      "       'mpg', 'engineSize'],\n",
      "      dtype='object')\n",
      "======================================================================\n",
      "unclean cclass.csv\n",
      "----------\n",
      "Index(['model', 'year', 'price', 'transmission', 'mileage', 'fuel type',\n",
      "       'engine size', 'mileage2', 'fuel type2', 'engine size2', 'reference'],\n",
      "      dtype='object')\n",
      "======================================================================\n",
      "unclean focus.csv\n",
      "----------\n",
      "Index(['model', 'year', 'price', 'transmission', 'mileage', 'fuel type',\n",
      "       'engine size', 'mileage2', 'fuel type2', 'engine size2', 'reference'],\n",
      "      dtype='object')\n",
      "======================================================================\n",
      "vauxhall.csv\n",
      "----------\n",
      "Index(['model', 'year', 'price', 'transmission', 'mileage', 'fuelType', 'tax',\n",
      "       'mpg', 'engineSize'],\n",
      "      dtype='object')\n",
      "======================================================================\n",
      "vw.csv\n",
      "----------\n",
      "Index(['model', 'year', 'price', 'transmission', 'mileage', 'fuelType', 'tax',\n",
      "       'mpg', 'engineSize'],\n",
      "      dtype='object')\n",
      "======================================================================\n"
     ]
    }
   ],
   "source": [
    "#check the columns in each csv\n",
    "\n",
    "for i in all_csv_files:\n",
    "    pd.read_csv(i)\n",
    "    print(i)\n",
    "    print('-'*10)\n",
    "    print(pd.read_csv(i).columns)\n",
    "    print('='*70)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4987449",
   "metadata": {},
   "source": [
    "> ### I found that:\n",
    "**1) these csv files have complete columns (audi.csv, bmw.csv, ford.csv, merc.csv, skoda.csv, toyota.csv, vauxhall.csv, vw.csv).**\n",
    "\n",
    "> #### 2) these csv files are missing tax column (cclass.csv, focus.csv)\n",
    "\n",
    "> #### 3) these csv files are missing mpg column (cclass.csv, focus.csv)\n",
    "\n",
    "> #### 4) hyundi.csv file have a column named tax(£) which should be tax.\n",
    "\n",
    "> #### 5) the two unclead files have a different columns than the others, so i have to deal with both of them separately.\n",
    "\n",
    "> #### 6) I need to add company column which contains company name for all files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "220777af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['audi.csv',\n",
       " 'bmw.csv',\n",
       " 'cclass.csv',\n",
       " 'focus.csv',\n",
       " 'ford.csv',\n",
       " 'hyundi.csv',\n",
       " 'merc.csv',\n",
       " 'skoda.csv',\n",
       " 'toyota.csv',\n",
       " 'vauxhall.csv',\n",
       " 'vw.csv']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Making the clean files in one list\n",
    "\n",
    "clean_csv_files = all_csv_files\n",
    "clean_csv_files.remove('unclean cclass.csv')\n",
    "clean_csv_files.remove('unclean focus.csv')\n",
    "clean_csv_files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "91f8422c",
   "metadata": {},
   "outputs": [],
   "source": [
    "clean_dataframes=[]\n",
    "\n",
    "for i in clean_csv_files:\n",
    "    \n",
    "    # Solving finding no.6 (add company column which contains company name)\n",
    "    company_name= i.split('.')[0]\n",
    "    df=pd.read_csv(i)\n",
    "    df['company name'] = company_name\n",
    "    \n",
    "    # solving finding no.4 (column named tax(£) should be tax)\n",
    "    if i == 'hyundi.csv':\n",
    "        df.rename(columns={'tax(£)': 'tax'}, inplace=True)\n",
    "        \n",
    "    # solving finding no.2 & no.3 ( missing columns in (cclass.csv, focus.csv))    \n",
    "    elif i == 'cclass.csv' or i == 'focus.csv' :\n",
    "        df['tax'] = np.nan\n",
    "        df['mpg'] = np.nan\n",
    "        \n",
    "    clean_dataframes.append(df)\n",
    "    \n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "df96f905",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>year</th>\n",
       "      <th>price</th>\n",
       "      <th>transmission</th>\n",
       "      <th>mileage</th>\n",
       "      <th>fuelType</th>\n",
       "      <th>tax</th>\n",
       "      <th>mpg</th>\n",
       "      <th>engineSize</th>\n",
       "      <th>company name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A1</td>\n",
       "      <td>2017</td>\n",
       "      <td>12500</td>\n",
       "      <td>Manual</td>\n",
       "      <td>15735</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>150.0</td>\n",
       "      <td>55.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>audi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A6</td>\n",
       "      <td>2016</td>\n",
       "      <td>16500</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>36203</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>20.0</td>\n",
       "      <td>64.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>audi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A1</td>\n",
       "      <td>2016</td>\n",
       "      <td>11000</td>\n",
       "      <td>Manual</td>\n",
       "      <td>29946</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>30.0</td>\n",
       "      <td>55.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>audi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A4</td>\n",
       "      <td>2017</td>\n",
       "      <td>16800</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>25952</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>145.0</td>\n",
       "      <td>67.3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>audi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A3</td>\n",
       "      <td>2019</td>\n",
       "      <td>17300</td>\n",
       "      <td>Manual</td>\n",
       "      <td>1998</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>145.0</td>\n",
       "      <td>49.6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>audi</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  model  year  price transmission  mileage fuelType    tax   mpg  engineSize  \\\n",
       "0    A1  2017  12500       Manual    15735   Petrol  150.0  55.4         1.4   \n",
       "1    A6  2016  16500    Automatic    36203   Diesel   20.0  64.2         2.0   \n",
       "2    A1  2016  11000       Manual    29946   Petrol   30.0  55.4         1.4   \n",
       "3    A4  2017  16800    Automatic    25952   Diesel  145.0  67.3         2.0   \n",
       "4    A3  2019  17300       Manual     1998   Petrol  145.0  49.6         1.0   \n",
       "\n",
       "  company name  \n",
       "0         audi  \n",
       "1         audi  \n",
       "2         audi  \n",
       "3         audi  \n",
       "4         audi  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merging all clean data in one data frame called (df_clean)\n",
    "\n",
    "df_clean = pd.concat(clean_dataframes, axis=0, ignore_index=True)\n",
    "df_clean.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "867d33e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(108540, 10)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8766fa33",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>price</th>\n",
       "      <th>mileage</th>\n",
       "      <th>tax</th>\n",
       "      <th>mpg</th>\n",
       "      <th>engineSize</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>108540.000000</td>\n",
       "      <td>108540.000000</td>\n",
       "      <td>108540.000000</td>\n",
       "      <td>99187.000000</td>\n",
       "      <td>99187.000000</td>\n",
       "      <td>108540.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2017.098028</td>\n",
       "      <td>16890.124046</td>\n",
       "      <td>23025.928469</td>\n",
       "      <td>120.299838</td>\n",
       "      <td>55.166825</td>\n",
       "      <td>1.661644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.130057</td>\n",
       "      <td>9756.266820</td>\n",
       "      <td>21176.423684</td>\n",
       "      <td>63.150926</td>\n",
       "      <td>16.138522</td>\n",
       "      <td>0.557058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1970.000000</td>\n",
       "      <td>450.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2016.000000</td>\n",
       "      <td>10229.500000</td>\n",
       "      <td>7491.750000</td>\n",
       "      <td>125.000000</td>\n",
       "      <td>47.100000</td>\n",
       "      <td>1.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2017.000000</td>\n",
       "      <td>14698.000000</td>\n",
       "      <td>17265.000000</td>\n",
       "      <td>145.000000</td>\n",
       "      <td>54.300000</td>\n",
       "      <td>1.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2019.000000</td>\n",
       "      <td>20940.000000</td>\n",
       "      <td>32236.000000</td>\n",
       "      <td>145.000000</td>\n",
       "      <td>62.800000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2060.000000</td>\n",
       "      <td>159999.000000</td>\n",
       "      <td>323000.000000</td>\n",
       "      <td>580.000000</td>\n",
       "      <td>470.800000</td>\n",
       "      <td>6.600000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                year          price        mileage           tax  \\\n",
       "count  108540.000000  108540.000000  108540.000000  99187.000000   \n",
       "mean     2017.098028   16890.124046   23025.928469    120.299838   \n",
       "std         2.130057    9756.266820   21176.423684     63.150926   \n",
       "min      1970.000000     450.000000       1.000000      0.000000   \n",
       "25%      2016.000000   10229.500000    7491.750000    125.000000   \n",
       "50%      2017.000000   14698.000000   17265.000000    145.000000   \n",
       "75%      2019.000000   20940.000000   32236.000000    145.000000   \n",
       "max      2060.000000  159999.000000  323000.000000    580.000000   \n",
       "\n",
       "                mpg     engineSize  \n",
       "count  99187.000000  108540.000000  \n",
       "mean      55.166825       1.661644  \n",
       "std       16.138522       0.557058  \n",
       "min        0.300000       0.000000  \n",
       "25%       47.100000       1.200000  \n",
       "50%       54.300000       1.600000  \n",
       "75%       62.800000       2.000000  \n",
       "max      470.800000       6.600000  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b0c8490a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b17e4ffc",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>year</th>\n",
       "      <th>price</th>\n",
       "      <th>transmission</th>\n",
       "      <th>mileage</th>\n",
       "      <th>fuel type</th>\n",
       "      <th>engine size</th>\n",
       "      <th>mileage2</th>\n",
       "      <th>fuel type2</th>\n",
       "      <th>engine size2</th>\n",
       "      <th>reference</th>\n",
       "      <th>company name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Focus</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>£8,000</td>\n",
       "      <td>Manual</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>0.999</td>\n",
       "      <td>38,852</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>/ad/25451236</td>\n",
       "      <td>focus</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Focus</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>£13,400</td>\n",
       "      <td>Manual</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>0.999</td>\n",
       "      <td>11,952</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>/ad/25466927</td>\n",
       "      <td>focus</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Focus</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>£14,600</td>\n",
       "      <td>Manual</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>1.498</td>\n",
       "      <td>22,142</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>/ad/25484363</td>\n",
       "      <td>focus</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Focus</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>£9,450</td>\n",
       "      <td>Manual</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>1.56</td>\n",
       "      <td>14,549</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>/ad/20444868</td>\n",
       "      <td>focus</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Focus</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>£9,999</td>\n",
       "      <td>Manual</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>1.56</td>\n",
       "      <td>7,010</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>/ad/22551057</td>\n",
       "      <td>focus</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    model    year     price transmission mileage fuel type engine size  \\\n",
       "0   Focus  2016.0    £8,000       Manual     NaN    Petrol       0.999   \n",
       "1   Focus  2019.0   £13,400       Manual     NaN    Petrol       0.999   \n",
       "2   Focus  2019.0   £14,600       Manual     NaN    Petrol       1.498   \n",
       "3   Focus  2016.0    £9,450       Manual     NaN    Diesel        1.56   \n",
       "4   Focus  2015.0    £9,999       Manual     NaN    Diesel        1.56   \n",
       "\n",
       "  mileage2 fuel type2 engine size2     reference company name  \n",
       "0   38,852        NaN          NaN  /ad/25451236        focus  \n",
       "1   11,952        NaN          NaN  /ad/25466927        focus  \n",
       "2   22,142        NaN          NaN  /ad/25484363        focus  \n",
       "3   14,549        NaN          NaN  /ad/20444868        focus  \n",
       "4    7,010        NaN          NaN  /ad/22551057        focus  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merging all unclean data in one data frame called (df_unclean).\n",
    "# unclean files (focus.csv & unclean cclass.csv) already have the same columns.\n",
    "\n",
    "\n",
    "unclean_csv_files=['unclean focus.csv' , 'unclean cclass.csv']\n",
    "\n",
    "unclean_dataframes=[]\n",
    "\n",
    "for i in unclean_csv_files:\n",
    "    \n",
    "    # Solving finding no.6 (add company column which contains company name)\n",
    "    company_name= i.split('.')[0].split(' ')[1]\n",
    "    df=pd.read_csv(i)\n",
    "    df['company name'] = company_name\n",
    "       \n",
    "    unclean_dataframes.append(df)\n",
    "    \n",
    "df_unclean = pd.concat(unclean_dataframes, axis=0, ignore_index=True)   \n",
    "df_unclean.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "03485b77",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9610, 12)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_unclean.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a60072a2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 9610 entries, 0 to 9609\n",
      "Data columns (total 12 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   model         9455 non-null   object \n",
      " 1   year          9363 non-null   float64\n",
      " 2   price         9455 non-null   object \n",
      " 3   transmission  9455 non-null   object \n",
      " 4   mileage       8537 non-null   object \n",
      " 5   fuel type     3517 non-null   object \n",
      " 6   engine size   9345 non-null   object \n",
      " 7   mileage2      9399 non-null   object \n",
      " 8   fuel type2    8537 non-null   object \n",
      " 9   engine size2  8537 non-null   object \n",
      " 10  reference     9455 non-null   object \n",
      " 11  company name  9610 non-null   object \n",
      "dtypes: float64(1), object(11)\n",
      "memory usage: 901.1+ KB\n"
     ]
    }
   ],
   "source": [
    "df_unclean.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e885fb8f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model\n",
      "---------------\n",
      " Focus      5548\n",
      " C Class    3907\n",
      "Name: model, dtype: int64\n",
      "==================================================\n",
      "year\n",
      "---------------\n",
      "2019.0    3184\n",
      "2017.0    1954\n",
      "2018.0    1428\n",
      "2016.0    1098\n",
      "2015.0     714\n",
      "2014.0     292\n",
      "2013.0     216\n",
      "2020.0     196\n",
      "2012.0      68\n",
      "2011.0      45\n",
      "2009.0      44\n",
      "2008.0      34\n",
      "2010.0      30\n",
      "2007.0      22\n",
      "2005.0      16\n",
      "2006.0       6\n",
      "2002.0       5\n",
      "2003.0       5\n",
      "2004.0       3\n",
      "1991.0       1\n",
      "1995.0       1\n",
      "1998.0       1\n",
      "Name: year, dtype: int64\n",
      "==================================================\n",
      "price\n",
      "---------------\n",
      " £11,000    78\n",
      " £16,000    77\n",
      " £12,000    68\n",
      " £13,000    59\n",
      " £16,998    55\n",
      "            ..\n",
      " £16,294     1\n",
      " £31,349     1\n",
      " £49,991     1\n",
      " £23,624     1\n",
      " £7,761      1\n",
      "Name: price, Length: 3140, dtype: int64\n",
      "==================================================\n",
      "transmission\n",
      "---------------\n",
      "Manual       4955\n",
      "Automatic    2273\n",
      "Semi-Auto    2226\n",
      "Other           1\n",
      "Name: transmission, dtype: int64\n",
      "==================================================\n",
      "mileage\n",
      "---------------\n",
      "1,000     81\n",
      "2,000     24\n",
      "100       23\n",
      "16        21\n",
      "6,000     21\n",
      "          ..\n",
      "16,714     1\n",
      "860        1\n",
      "24,700     1\n",
      "8,746      1\n",
      "6,374      1\n",
      "Name: mileage, Length: 6896, dtype: int64\n",
      "==================================================\n",
      "fuel type\n",
      "---------------\n",
      "Petrol    552\n",
      "14        425\n",
      "Diesel    363\n",
      "31        311\n",
      "32        240\n",
      "11        197\n",
      "38        175\n",
      "26        165\n",
      "15        153\n",
      "37        101\n",
      "24         90\n",
      "16         79\n",
      "30         76\n",
      "36         70\n",
      "12         69\n",
      "25         67\n",
      "33         66\n",
      "29         57\n",
      "10         44\n",
      "34         33\n",
      "13         24\n",
      "22         23\n",
      "35         21\n",
      "40         19\n",
      "39         17\n",
      "43         13\n",
      "27         12\n",
      "21          7\n",
      "44          6\n",
      "18          6\n",
      "19          5\n",
      "17          4\n",
      "46          4\n",
      "9           4\n",
      "45          3\n",
      "47          3\n",
      "28          3\n",
      "Hybrid      3\n",
      "20          2\n",
      "8           2\n",
      "7           1\n",
      "48          1\n",
      "6           1\n",
      "Name: fuel type, dtype: int64\n",
      "==================================================\n",
      "engine size\n",
      "---------------\n",
      "£145    4785\n",
      "£20     1013\n",
      "£0       630\n",
      "£30      564\n",
      "£150     511\n",
      "        ... \n",
      "2100       1\n",
      "1498       1\n",
      "£120       1\n",
      "£290       1\n",
      "£185       1\n",
      "Name: engine size, Length: 68, dtype: int64\n",
      "==================================================\n",
      "mileage2\n",
      "---------------\n",
      "60.1      1210\n",
      "74.3       928\n",
      "61.4       722\n",
      "64.2       721\n",
      "67.3       517\n",
      "          ... \n",
      "28.3         1\n",
      "21,154       1\n",
      "77,368       1\n",
      "22,142       1\n",
      "71,000       1\n",
      "Name: mileage2, Length: 313, dtype: int64\n",
      "==================================================\n",
      "fuel type2\n",
      "---------------\n",
      "Petrol    4457\n",
      "Diesel    3923\n",
      "Hybrid     150\n",
      "Other        7\n",
      "Name: fuel type2, dtype: int64\n",
      "==================================================\n",
      "engine size2\n",
      "---------------\n",
      "1          1972\n",
      "2          1929\n",
      "1.5        1636\n",
      "2.1         985\n",
      "1.6         459\n",
      "           ... \n",
      "2.996         1\n",
      "3606          1\n",
      "2.7           1\n",
      "2497          1\n",
      "1596 cc       1\n",
      "Name: engine size2, Length: 82, dtype: int64\n",
      "==================================================\n",
      "reference\n",
      "---------------\n",
      "/ad/22345751    1\n",
      "/ad/25284313    1\n",
      "/ad/25452888    1\n",
      "/ad/25174789    1\n",
      "/ad/25484968    1\n",
      "               ..\n",
      "/ad/25134422    1\n",
      "/ad/25464910    1\n",
      "/ad/25407248    1\n",
      "/ad/25366754    1\n",
      "/ad/25272823    1\n",
      "Name: reference, Length: 9455, dtype: int64\n",
      "==================================================\n",
      "company name\n",
      "---------------\n",
      "focus     5604\n",
      "cclass    4006\n",
      "Name: company name, dtype: int64\n",
      "==================================================\n"
     ]
    }
   ],
   "source": [
    "for col in df_unclean.columns:\n",
    "    print(col)\n",
    "    print('-'*15)\n",
    "    print(df_unclean[col].value_counts())\n",
    "    print('='*50)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5fb50570",
   "metadata": {},
   "source": [
    "### From the results above i found that:\n",
    "\n",
    "#### These columns are ok  [Company name, fuel type2, model, year, transmission]\n",
    "\n",
    "    •\t1- fuel type2 should be fuel type column because it has the correct data\n",
    "    •\t2- should remove pound symbol from price column values\n",
    "    •\t3- column reference should be dropped\n",
    "\n",
    "##### now we have 6 columns \n",
    "\n",
    "\n",
    "##### remaining cols are: [ 'mileage', ‘mileage2‘,  'tax', 'mpg', 'engineSize', 'engineSize2', ‘fuel type’]\n",
    "    •\t4- 'engineSize2' is ok but it have some values in cc instead of liter so it should be converted as 1 liter = 1000 cc, and    cc symbol should be removed, and this column should be the 'engineSize' column.\n",
    "    •\t5- Get mileage from taking the biggest value of mileage&mileage2 columns.\n",
    "    \t\n",
    "\n",
    "    •\t Until this stage the remaining columnss are: ['tax',  'mpg']\n",
    "    •\t6- Note that in df_clean the range of tax column was from 0 to 580 and the mean was 120, so I choose the engine size column in df_unclean to be tax column in df_unclean because the data in it fits to be tax.\n",
    "    •\t7- fuel type column should be dropped\n",
    "    •   8- creating mpg column \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e06031a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 3- Dropping reference column\n",
    "df_unclean.drop('reference',axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c9cf1276",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2- Clean price column \n",
    "df_unclean['price'] = [str(i).replace(\",\", \"\").strip() for i in df_unclean['price']]\n",
    "df_unclean['price'] = [float(str(i).replace(\"£\", \"\").strip()) for i in df_unclean['price']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "308e3fde",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 8000., 13400., 14600., ..., 18339., 25450., 23600.])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_unclean['price'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "7792a658",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         0.999\n",
       "1         0.999\n",
       "2         1.498\n",
       "3         1.560\n",
       "4         1.560\n",
       "         ...   \n",
       "9605    150.000\n",
       "9606    150.000\n",
       "9607     20.000\n",
       "9608    145.000\n",
       "9609     30.000\n",
       "Name: tax, Length: 9610, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 6- making engine size column as tax column and clean it\n",
    "\n",
    "df_unclean.rename(columns={'engine size': 'tax'}, inplace=True)\n",
    "df_unclean['tax'] = [str(i).replace(\"£\", \"\").strip() for i in df_unclean['tax']]\n",
    "df_unclean['tax'] = [str(i).lower().replace(\"cc\", \"\").strip() for i in df_unclean['tax']]\n",
    "df_unclean['tax'] = [str(i).replace(\",\", \"\").strip() for i in df_unclean['tax']]\n",
    "df_unclean['tax'] = [float(str(i).strip()) for i in df_unclean['tax']]\n",
    "df_unclean['tax']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "6c3c07d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 7- fuel type column should be dropped\n",
    "df_unclean.drop('fuel type', axis=1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "870b7988",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 8- creating mpg column \n",
    "df_unclean['mpg']= np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "536b48f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([nan, 'Petrol', 'Diesel', 'Other', 'Hybrid'], dtype=object)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1- fuel type2 should be fuel type column\n",
    "df_unclean.rename(columns={'fuel type2' : 'fuelType'}, inplace= True)\n",
    "df_unclean['fuelType'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "0d6e46d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 4- renaming 'engineSize2' column and clean it\n",
    "\n",
    "df_unclean.rename(columns={'engine size2' : 'engineSize'}, inplace= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "fc776df9",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>year</th>\n",
       "      <th>price</th>\n",
       "      <th>transmission</th>\n",
       "      <th>mileage</th>\n",
       "      <th>tax</th>\n",
       "      <th>mileage2</th>\n",
       "      <th>fuelType</th>\n",
       "      <th>engineSize</th>\n",
       "      <th>company name</th>\n",
       "      <th>mpg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Focus</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>8000.0</td>\n",
       "      <td>Manual</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.999</td>\n",
       "      <td>38,852</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>focus</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Focus</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>13400.0</td>\n",
       "      <td>Manual</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.999</td>\n",
       "      <td>11,952</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>focus</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Focus</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>14600.0</td>\n",
       "      <td>Manual</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.498</td>\n",
       "      <td>22,142</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>focus</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Focus</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>9450.0</td>\n",
       "      <td>Manual</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.560</td>\n",
       "      <td>14,549</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>focus</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Focus</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>9999.0</td>\n",
       "      <td>Manual</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.560</td>\n",
       "      <td>7,010</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>focus</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9605</th>\n",
       "      <td>C Class</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>14700.0</td>\n",
       "      <td>Manual</td>\n",
       "      <td>31,357</td>\n",
       "      <td>150.000</td>\n",
       "      <td>70.6</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>1.598</td>\n",
       "      <td>cclass</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9606</th>\n",
       "      <td>C Class</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>18500.0</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>28,248</td>\n",
       "      <td>150.000</td>\n",
       "      <td>64.2</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>2.143</td>\n",
       "      <td>cclass</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9607</th>\n",
       "      <td>C Class</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>11900.0</td>\n",
       "      <td>Manual</td>\n",
       "      <td>48,055</td>\n",
       "      <td>20.000</td>\n",
       "      <td>65.7</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>2.143</td>\n",
       "      <td>cclass</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9608</th>\n",
       "      <td>C Class</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>11300.0</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>49,865</td>\n",
       "      <td>145.000</td>\n",
       "      <td>56.5</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>2.143</td>\n",
       "      <td>cclass</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9609</th>\n",
       "      <td>C Class</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>14800.0</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>55,445</td>\n",
       "      <td>30.000</td>\n",
       "      <td>64.2</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>2.143</td>\n",
       "      <td>cclass</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9610 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         model    year    price transmission mileage      tax mileage2  \\\n",
       "0        Focus  2016.0   8000.0       Manual     NaN    0.999   38,852   \n",
       "1        Focus  2019.0  13400.0       Manual     NaN    0.999   11,952   \n",
       "2        Focus  2019.0  14600.0       Manual     NaN    1.498   22,142   \n",
       "3        Focus  2016.0   9450.0       Manual     NaN    1.560   14,549   \n",
       "4        Focus  2015.0   9999.0       Manual     NaN    1.560    7,010   \n",
       "...        ...     ...      ...          ...     ...      ...      ...   \n",
       "9605   C Class  2017.0  14700.0       Manual  31,357  150.000     70.6   \n",
       "9606   C Class  2018.0  18500.0    Automatic  28,248  150.000     64.2   \n",
       "9607   C Class  2014.0  11900.0       Manual  48,055   20.000     65.7   \n",
       "9608   C Class  2014.0  11300.0    Automatic  49,865  145.000     56.5   \n",
       "9609   C Class  2014.0  14800.0    Automatic  55,445   30.000     64.2   \n",
       "\n",
       "     fuelType engineSize company name  mpg  \n",
       "0         NaN        NaN        focus  NaN  \n",
       "1         NaN        NaN        focus  NaN  \n",
       "2         NaN        NaN        focus  NaN  \n",
       "3         NaN        NaN        focus  NaN  \n",
       "4         NaN        NaN        focus  NaN  \n",
       "...       ...        ...          ...  ...  \n",
       "9605   Diesel      1.598       cclass  NaN  \n",
       "9606   Diesel      2.143       cclass  NaN  \n",
       "9607   Diesel      2.143       cclass  NaN  \n",
       "9608   Diesel      2.143       cclass  NaN  \n",
       "9609   Diesel      2.143       cclass  NaN  \n",
       "\n",
       "[9610 rows x 11 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_unclean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "ec10261d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([nan, '0.999', '1.499', '1.596', '1.997', '1', '1000', '1500',\n",
       "       '1.498', '2', '1.6', '1.5', '1.56', '1.8', '1600', '1999', '2.3',\n",
       "       '1596', '1798', '999', '1560', '1498', '998', '1997', '1499',\n",
       "       '1560 cc', '2000', '1988', '2521', '2261', '0', '2300', '0.998',\n",
       "       '1496', '1753', '1989', '2.5', '1596 cc', '2.29', 'Unknown',\n",
       "       '2.521', '1388', '1.0T', '2.1', '2143', '4', '1991', '1595', '6.2',\n",
       "       '2145', '1950', '1796', '3982', '3', '1598', '2996', '2100',\n",
       "       '1597', '1497', '1.991', '2148', '2.14', '2.143', '3498', '3000',\n",
       "       '2.7', '2685', '2.2', '2.996', '2962', '1998', '1461', '3606',\n",
       "       '4000', '1.598', '2497', '2987', '1.497', '2.15', '5439', '2597',\n",
       "       '1800', '6208'], dtype=object)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_unclean['engineSize'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "01e5d649",
   "metadata": {},
   "outputs": [],
   "source": [
    "#clean engineSize column\n",
    "\n",
    "df_unclean['engineSize'] = [str(i).replace('cc','').strip()  for i in df_unclean['engineSize']]\n",
    "df_unclean['engineSize'] = [str(i).replace('T','').strip()  for i in df_unclean['engineSize']]\n",
    "df_unclean['engineSize'] = [str(i).replace('Unknown','').strip()   for i in df_unclean['engineSize']]\n",
    "df_unclean['engineSize'] = pd.to_numeric(df_unclean['engineSize'], errors='coerce')\n",
    "\n",
    "# converting cc(cubic centimeters) to liters (1 liter = 1000 cc)\n",
    "df_unclean['engineSize'] = df_unclean.apply(lambda x: x['engineSize']/1000 if x['engineSize']> 20 else x['engineSize'] , axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "46c7cb7c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  nan, 0.999, 1.499, 1.596, 1.997, 1.   , 1.5  , 1.498, 2.   ,\n",
       "       1.6  , 1.56 , 1.8  , 1.999, 2.3  , 1.798, 0.998, 1.988, 2.521,\n",
       "       2.261, 0.   , 1.496, 1.753, 1.989, 2.5  , 2.29 , 1.388, 2.1  ,\n",
       "       2.143, 4.   , 1.991, 1.595, 6.2  , 2.145, 1.95 , 1.796, 3.982,\n",
       "       3.   , 1.598, 2.996, 1.597, 1.497, 2.148, 2.14 , 3.498, 2.7  ,\n",
       "       2.685, 2.2  , 2.962, 1.998, 1.461, 3.606, 2.497, 2.987, 2.15 ,\n",
       "       5.439, 2.597, 6.208])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_unclean['engineSize'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "e633ca2f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#clean milage columns\n",
    "mileage=['mileage','mileage2']\n",
    "for i in mileage:\n",
    "    df_unclean[i] = [str(i).split('.')[0] for i in df_unclean[i]]\n",
    "    df_unclean[i] = [str(i).replace(',', '') for i in df_unclean[i]]\n",
    "    df_unclean[i] = [str(i).replace('Unknown', '') for i in df_unclean[i]]\n",
    "    df_unclean[i]=df_unclean[i].replace('nan', '')\n",
    "    df_unclean[i] = pd.to_numeric(df_unclean[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "daea1d17",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 5- Get mileage by taking the biggest value of mileage&mileage2 columns\n",
    "\n",
    "df_unclean[\"mileage\"] = df_unclean.apply(lambda x: x[\"mileage\"] if x[\"mileage\"]!= np.NaN and x[\"mileage\"]>x[\"mileage2\"] else x[\"mileage2\"], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "850f4dff",
   "metadata": {},
   "outputs": [],
   "source": [
    "#drop old column\n",
    "df_unclean.drop([\"mileage2\"], inplace=True, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "d108768c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>year</th>\n",
       "      <th>price</th>\n",
       "      <th>transmission</th>\n",
       "      <th>mileage</th>\n",
       "      <th>tax</th>\n",
       "      <th>fuelType</th>\n",
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       "      <th>0</th>\n",
       "      <td>Focus</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>8000.0</td>\n",
       "      <td>Manual</td>\n",
       "      <td>38852.0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>1</th>\n",
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       "      <td>0.999</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>focus</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Focus</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>14600.0</td>\n",
       "      <td>Manual</td>\n",
       "      <td>22142.0</td>\n",
       "      <td>1.498</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>focus</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Focus</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>9450.0</td>\n",
       "      <td>Manual</td>\n",
       "      <td>14549.0</td>\n",
       "      <td>1.560</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>focus</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Focus</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>9999.0</td>\n",
       "      <td>Manual</td>\n",
       "      <td>7010.0</td>\n",
       "      <td>1.560</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>focus</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>9605</th>\n",
       "      <td>C Class</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>14700.0</td>\n",
       "      <td>Manual</td>\n",
       "      <td>31357.0</td>\n",
       "      <td>150.000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>1.598</td>\n",
       "      <td>cclass</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9606</th>\n",
       "      <td>C Class</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>18500.0</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>28248.0</td>\n",
       "      <td>150.000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>2.143</td>\n",
       "      <td>cclass</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9607</th>\n",
       "      <td>C Class</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>11900.0</td>\n",
       "      <td>Manual</td>\n",
       "      <td>48055.0</td>\n",
       "      <td>20.000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>2.143</td>\n",
       "      <td>cclass</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>9608</th>\n",
       "      <td>C Class</td>\n",
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       "      <td>11300.0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>9609</th>\n",
       "      <td>C Class</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>14800.0</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>55445.0</td>\n",
       "      <td>30.000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>2.143</td>\n",
       "      <td>cclass</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9610 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         model    year    price transmission  mileage      tax fuelType  \\\n",
       "0        Focus  2016.0   8000.0       Manual  38852.0    0.999      NaN   \n",
       "1        Focus  2019.0  13400.0       Manual  11952.0    0.999      NaN   \n",
       "2        Focus  2019.0  14600.0       Manual  22142.0    1.498      NaN   \n",
       "3        Focus  2016.0   9450.0       Manual  14549.0    1.560      NaN   \n",
       "4        Focus  2015.0   9999.0       Manual   7010.0    1.560      NaN   \n",
       "...        ...     ...      ...          ...      ...      ...      ...   \n",
       "9605   C Class  2017.0  14700.0       Manual  31357.0  150.000   Diesel   \n",
       "9606   C Class  2018.0  18500.0    Automatic  28248.0  150.000   Diesel   \n",
       "9607   C Class  2014.0  11900.0       Manual  48055.0   20.000   Diesel   \n",
       "9608   C Class  2014.0  11300.0    Automatic  49865.0  145.000   Diesel   \n",
       "9609   C Class  2014.0  14800.0    Automatic  55445.0   30.000   Diesel   \n",
       "\n",
       "      engineSize company name  mpg  \n",
       "0            NaN        focus  NaN  \n",
       "1            NaN        focus  NaN  \n",
       "2            NaN        focus  NaN  \n",
       "3            NaN        focus  NaN  \n",
       "4            NaN        focus  NaN  \n",
       "...          ...          ...  ...  \n",
       "9605       1.598       cclass  NaN  \n",
       "9606       2.143       cclass  NaN  \n",
       "9607       2.143       cclass  NaN  \n",
       "9608       2.143       cclass  NaN  \n",
       "9609       2.143       cclass  NaN  \n",
       "\n",
       "[9610 rows x 10 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_unclean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "759d0321",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 9610 entries, 0 to 9609\n",
      "Data columns (total 10 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   model         9455 non-null   object \n",
      " 1   year          9363 non-null   float64\n",
      " 2   price         9455 non-null   float64\n",
      " 3   transmission  9455 non-null   object \n",
      " 4   mileage       9398 non-null   float64\n",
      " 5   tax           9345 non-null   float64\n",
      " 6   fuelType      8537 non-null   object \n",
      " 7   engineSize    8531 non-null   float64\n",
      " 8   company name  9610 non-null   object \n",
      " 9   mpg           0 non-null      float64\n",
      "dtypes: float64(6), object(4)\n",
      "memory usage: 750.9+ KB\n"
     ]
    }
   ],
   "source": [
    "df_unclean.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "c06e7781",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 108540 entries, 0 to 108539\n",
      "Data columns (total 10 columns):\n",
      " #   Column        Non-Null Count   Dtype  \n",
      "---  ------        --------------   -----  \n",
      " 0   model         108540 non-null  object \n",
      " 1   year          108540 non-null  int64  \n",
      " 2   price         108540 non-null  int64  \n",
      " 3   transmission  108540 non-null  object \n",
      " 4   mileage       108540 non-null  int64  \n",
      " 5   fuelType      108540 non-null  object \n",
      " 6   tax           99187 non-null   float64\n",
      " 7   mpg           99187 non-null   float64\n",
      " 8   engineSize    108540 non-null  float64\n",
      " 9   company name  108540 non-null  object \n",
      "dtypes: float64(3), int64(3), object(4)\n",
      "memory usage: 8.3+ MB\n"
     ]
    }
   ],
   "source": [
    "df_clean.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6c49d2a",
   "metadata": {},
   "source": [
    "> **Now df_clean and df_unclean have the same column names and data types**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "090608f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>year</th>\n",
       "      <th>price</th>\n",
       "      <th>transmission</th>\n",
       "      <th>mileage</th>\n",
       "      <th>fuelType</th>\n",
       "      <th>tax</th>\n",
       "      <th>mpg</th>\n",
       "      <th>engineSize</th>\n",
       "      <th>company name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A1</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>12500.0</td>\n",
       "      <td>Manual</td>\n",
       "      <td>15735.0</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>150.0</td>\n",
       "      <td>55.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>audi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A6</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>16500.0</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>36203.0</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>20.0</td>\n",
       "      <td>64.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>audi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A1</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>11000.0</td>\n",
       "      <td>Manual</td>\n",
       "      <td>29946.0</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>30.0</td>\n",
       "      <td>55.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>audi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A4</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>16800.0</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>25952.0</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>145.0</td>\n",
       "      <td>67.3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>audi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A3</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>17300.0</td>\n",
       "      <td>Manual</td>\n",
       "      <td>1998.0</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>145.0</td>\n",
       "      <td>49.6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>audi</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  model    year    price transmission  mileage fuelType    tax   mpg  \\\n",
       "0    A1  2017.0  12500.0       Manual  15735.0   Petrol  150.0  55.4   \n",
       "1    A6  2016.0  16500.0    Automatic  36203.0   Diesel   20.0  64.2   \n",
       "2    A1  2016.0  11000.0       Manual  29946.0   Petrol   30.0  55.4   \n",
       "3    A4  2017.0  16800.0    Automatic  25952.0   Diesel  145.0  67.3   \n",
       "4    A3  2019.0  17300.0       Manual   1998.0   Petrol  145.0  49.6   \n",
       "\n",
       "   engineSize company name  \n",
       "0         1.4         audi  \n",
       "1         2.0         audi  \n",
       "2         1.4         audi  \n",
       "3         2.0         audi  \n",
       "4         1.0         audi  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Concatenating DataFrames\n",
    "df = pd.concat([df_clean, df_unclean], axis=0, ignore_index=True)  \n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "6798be94",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(118150, 10)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "37fcbb1a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2017., 2016., 2019., 2015., 2014., 2018., 2013., 2020., 2004.,\n",
       "       2009., 2012., 2010., 2007., 2011., 2008., 2003., 2005., 2002.,\n",
       "       2006., 1998., 1997., 2001., 2000., 1999., 1996., 1991., 1995.,\n",
       "       2060., 1970.,   nan])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.year.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81f854bb",
   "metadata": {},
   "source": [
    "## Missing Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "46538a46",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "model             155\n",
       "year              247\n",
       "price             155\n",
       "transmission      155\n",
       "mileage           212\n",
       "fuelType         1073\n",
       "tax              9618\n",
       "mpg             18963\n",
       "engineSize       1079\n",
       "company name        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "aeb29323",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['mileage'].fillna(df['mileage'].mean(), inplace = True)\n",
    "df['tax'].fillna(df['tax'].mean(), inplace = True)\n",
    "df['mpg'].fillna(df['mpg'].mean(), inplace = True)\n",
    "df['model'].fillna(df['model'].mode()[0], inplace = True)\n",
    "df['transmission'].fillna(df['transmission'].mode()[0], inplace = True)\n",
    "df['fuelType'].fillna(df['fuelType'].mode()[0], inplace = True)\n",
    "df['year'].fillna(df['year'].mode()[0], inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "370c8ddb",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.impute import KNNImputer\n",
    "imputer = KNNImputer()\n",
    "\n",
    "df['price'] = imputer.fit_transform(df[['price']])\n",
    "df['engineSize'] = imputer.fit_transform(df[['engineSize']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "33c58872",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "model           0\n",
       "year            0\n",
       "price           0\n",
       "transmission    0\n",
       "mileage         0\n",
       "fuelType        0\n",
       "tax             0\n",
       "mpg             0\n",
       "engineSize      0\n",
       "company name    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b559492",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis  (EDA)\n",
    "\n",
    "## Data cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "8968073f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>price</th>\n",
       "      <th>mileage</th>\n",
       "      <th>tax</th>\n",
       "      <th>mpg</th>\n",
       "      <th>engineSize</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>118150.000000</td>\n",
       "      <td>118150.000000</td>\n",
       "      <td>118150.000000</td>\n",
       "      <td>118150.00000</td>\n",
       "      <td>118150.000000</td>\n",
       "      <td>118150.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2017.110410</td>\n",
       "      <td>16960.046561</td>\n",
       "      <td>22967.101460</td>\n",
       "      <td>119.96505</td>\n",
       "      <td>55.166825</td>\n",
       "      <td>1.661791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.135371</td>\n",
       "      <td>9653.502909</td>\n",
       "      <td>21128.287743</td>\n",
       "      <td>76.43951</td>\n",
       "      <td>14.786791</td>\n",
       "      <td>0.554135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1970.000000</td>\n",
       "      <td>450.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2016.000000</td>\n",
       "      <td>10395.000000</td>\n",
       "      <td>7529.000000</td>\n",
       "      <td>119.96505</td>\n",
       "      <td>47.900000</td>\n",
       "      <td>1.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2017.000000</td>\n",
       "      <td>14980.000000</td>\n",
       "      <td>17120.000000</td>\n",
       "      <td>145.00000</td>\n",
       "      <td>55.166825</td>\n",
       "      <td>1.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2019.000000</td>\n",
       "      <td>20950.000000</td>\n",
       "      <td>32082.000000</td>\n",
       "      <td>145.00000</td>\n",
       "      <td>60.100000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2060.000000</td>\n",
       "      <td>159999.000000</td>\n",
       "      <td>323000.000000</td>\n",
       "      <td>5400.00000</td>\n",
       "      <td>470.800000</td>\n",
       "      <td>6.600000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                year          price        mileage           tax  \\\n",
       "count  118150.000000  118150.000000  118150.000000  118150.00000   \n",
       "mean     2017.110410   16960.046561   22967.101460     119.96505   \n",
       "std         2.135371    9653.502909   21128.287743      76.43951   \n",
       "min      1970.000000     450.000000       1.000000       0.00000   \n",
       "25%      2016.000000   10395.000000    7529.000000     119.96505   \n",
       "50%      2017.000000   14980.000000   17120.000000     145.00000   \n",
       "75%      2019.000000   20950.000000   32082.000000     145.00000   \n",
       "max      2060.000000  159999.000000  323000.000000    5400.00000   \n",
       "\n",
       "                 mpg     engineSize  \n",
       "count  118150.000000  118150.000000  \n",
       "mean       55.166825       1.661791  \n",
       "std        14.786791       0.554135  \n",
       "min         0.300000       0.000000  \n",
       "25%        47.900000       1.200000  \n",
       "50%        55.166825       1.600000  \n",
       "75%        60.100000       2.000000  \n",
       "max       470.800000       6.600000  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "b49904ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>year</th>\n",
       "      <th>price</th>\n",
       "      <th>transmission</th>\n",
       "      <th>mileage</th>\n",
       "      <th>fuelType</th>\n",
       "      <th>tax</th>\n",
       "      <th>mpg</th>\n",
       "      <th>engineSize</th>\n",
       "      <th>company name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>48528</th>\n",
       "      <td>Fiesta</td>\n",
       "      <td>2060.0</td>\n",
       "      <td>6495.0</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>54807.0</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>205.0</td>\n",
       "      <td>42.8</td>\n",
       "      <td>1.4</td>\n",
       "      <td>ford</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         model    year   price transmission  mileage fuelType    tax   mpg  \\\n",
       "48528   Fiesta  2060.0  6495.0    Automatic  54807.0   Petrol  205.0  42.8   \n",
       "\n",
       "       engineSize company name  \n",
       "48528         1.4         ford  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['year']==2060]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "721e82f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop(labels=48528, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "ff58ca49",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "ab28c037",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='mileage'>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Kj62f0znnnNfo/g3/6tuGz91w/gMHDo5Bg4aUbg8aNKQ0dsPXqX4Nu9PweerX0fB2wzF2vO+enmtJdl7vlypi3p9Ww3UPHPj10vuyOdZ84IEHNstrWCnvoXqVNt96lTrvplTaWsQGsE+p/xPxXX1SUF3dLiIiunY9YpdjdOzYsdEPQPfpc3R88YuHRUTs8gejO3bcLzp06BCnnz66dKxLly4REXH88f2jqqoqxowZGx06dIiDDz4k2rdvH127finGjZsQRx7ZM0aOHBXjxk2I9u3bR/v27eP008+MiIh27dqV7rf//n8UERH77bd/dO/eI8aNmxCHH/6laNfuM1FVVRUXXnhxaayIiHHjJkRNTU2MHz+x0fGI7X9yVn/sggv+13/f/+KdztXf7tatRxx++Jeie/cepU9HjjyyZ4wf/zdRU1MTF1wwPmpqauKiiy6JiIhRo7bPv+GnGjs+97hxl0S3bj0ajdnw9vjxfxNVVVXxxS8e1uh12lNNrWNXY3zScy3Jx1lva9Jwnc295uYar9L2otLmW2/EiNPiy1/+csXNuymVtAdVxW6+VLtq1YdN/s0ge9vkyVdEu3ZtP/XPbHTsemKzzWn90qciIpp1zE8yhx5+ZuNTqf+bvFrya1gOLX3faJp9qzz2rDLZt8pk35pfmzZV0aXL/rs+vxfnAgAA7EPEBgAAkEJsAAAAKcQGAACQQmwAAAApxAYAAJBCbAAAACnEBgAAkEJsAAAAKcQGAACQQmwAAAApxAYAAJBCbAAAACnEBgAAkEJsAAAAKcQGAACQQmwAAAApxAYAAJBCbAAAACnEBgAAkEJsAAAAKcQGAACQQmwAAAApxAYAAJBCbAAAACnEBgAAkEJsAAAAKcQGAACQQmwAAAApxAYAAJBCbAAAACnEBgAAkEJsAAAAKcQGAACQQmwAAAApxAYAAJBCbAAAACnEBgAAkEJsAAAAKcQGAACQQmwAAAApxAYAAJBCbAAAACnEBgAAkEJsAAAAKcQGAACQQmwAAAApxAYAAJBCbAAAACnEBgAAkEJsAAAAKcQGAACQQmwAAAApxAYAAJBCbAAAACnEBgAAkEJsAAAAKcQGAACQQmwAAAApxAYAAJBCbAAAACnEBgAAkEJsAAAAKcQGAACQQmwAAAApxAYAAJBCbAAAACnEBgAAkEJsAAAAKcQGAACQQmwAAAApxAYAAJBCbAAAACnEBgAAkEJsAAAAKcQGAACQQmwAAAApxAYAAJBCbAAAACnEBgAAkEJsAAAAKcQGAACQQmwAAAApxAYAAJBCbAAAACnEBgAAkEJsAAAAKcQGAACQQmwAAAApxAYAAJBCbAAAACnEBgAAkEJsAAAAKcQGAACQQmwAAAApxAYAAJBCbAAAACnEBgAAkEJsAAAAKcQGAACQQmwAAAApxAYAAJCiutwT2FMDBnw9DjigptzToBUaMODr5Z4CAECrVDGxccIJg+Kgg/4o3n33g3JPhVbmhBMGlXsKAACtkq9RAQAAKcQGAACQQmwAAAApxAYAAJBCbAAAACnEBgAAkEJsAAAAKcQGAACQQmwAAAApxAYAAJBCbAAAACnEBgAAkEJsAAAAKcQGAACQQmwAAAApxAYAAJBCbAAAACnEBgAAkEJsAAAAKcQGAACQQmwAAAApxAYAAJBCbAAAACnEBgAAkEJsAAAAKcQGAACQQmwAAAApxAYAAJBCbAAAACnEBgAAkEJsAAAAKcQGAACQQmwAAAApxAYAAJBCbAAAACnEBgAAkEJsAAAAKcQGAACQQmwAAAApxAYAAJBCbAAAACnEBgAAkEJsAAAAKcQGAACQQmwAAAApxAYAAJBCbAAAACnEBgAAkEJsAAAAKcQGAACQQmwAAAApxAYAAJBCbAAAACnEBgAAkEJsAAAAKcQGAACQQmwAAAApxAYAAJBCbAAAACnEBgAAkEJsAAAAKcQGAACQQmwAAAApxAYAAJBCbAAAACnEBgAAkEJsAAAAKcQGAACQQmwAAAApxAYAAJBCbAAAACnEBgAAkEJsAAAAKcQGAACQQmwAAAApxAYAAJBCbAAAACnEBgAAkEJsAAAAKcQGAACQQmwAAAApxAYAAJBCbAAAACnEBgAAkEJsAAAAKcQGAACQQmwAAAApxAYAAJBCbAAAACnEBgAAkEJsAAAAKcQGAACQQmwAAAApxAYAAJBCbAAAACnEBgAAkEJsAAAAKcQGAACQQmwAAAApxAYAAJBCbAAAACnEBgAAkEJsAAAAKcQGAACQQmwAAAApxAYAAJCiutwT2JvqNq6J9UufatbxIqJZx/xkc/hc2Z4fAAB2ZZ+JjcMO69rsY77//vaXr1Onzs0+9p77XMraAADg09pnYuPss88t9xQAAGCf4mc2AACAFGIDAABIITYAAIAUYgMAAEghNgAAgBRiAwAASCE2AACAFGIDAABIITYAAIAUYgMAAEghNgAAgBRiAwAASCE2AACAFGIDAABIITYAAIAUYgMAAEghNgAAgBRiAwAASCE2AACAFGIDAABIITYAAIAUYgMAAEghNgAAgBRiAwAASCE2AACAFGIDAABIITYAAIAUYgMAAEghNgAAgBRiAwAASCE2AACAFGIDAABIITYAAIAUYgMAAEghNgAAgBRiAwAASCE2AACAFGIDAABIITYAAIAUYgMAAEghNgAAgBRiAwAASCE2AACAFGIDAABIITYAAIAUYgMAAEghNgAAgBRiAwAASCE2AACAFGIDAABIITYAAIAU1bu7Q5s2VXtjHnuspc2HPWPfKpN9q0z2rfLYs8pk3yqTfWteu3s9q4qiKPbSXAAAgH2Ir1EBAAApxAYAAJBCbAAAACnEBgAAkEJsAAAAKcQGAACQQmwAAAApxAYAAJBCbAAAACkqIjZef/31OPPMM6O2tjZGjBgRr7zySrmntM946KGHolevXnHMMceU/nnkkUdi8+bNcdlll0W/fv3i+OOPj9tuu63R4x5//PEYOnRo1NbWxoUXXhirVq0qnVuxYkWcf/75ccwxx8RJJ50Uzz77bOlcURRx/fXXR//+/eO4446LK6+8MrZu3brX1lvpXnnllejfv3/pdrn2ad68eTFixIiora2N0aNHx7JlyxJXXfma2rfevXs3uu7Gjh1bOm/fyuu5556LUaNGRd++fWPo0KHxwAMPRITrraX7qH1zvbVczzzzTIwYMaL0GrveKlDRwm3atKkYMmRIcddddxWbN28uZs6cWRx33HHFBx98UO6p7RN+8IMfFD/60Y92On7NNdcU3/rWt4o1a9YUb731VjFs2LDikUceKYqiKBYuXFjU1tYWL730UrFx48biiiuuKMaMGVN67JlnnllcddVVxaZNm4rnn3++OOaYY4ply5YVRVEU999/f/GXf/mXxcqVK4tVq1YVo0ePLm688ca9stZKtm3btmL69OnFscceWxx77LGl4+XYp1WrVhV9+/YtnnjiiWLz5s3FT3/60+Kkk04q6urq9t4LUiF2tW+vvPJK8bWvfa3Jx9i38lqxYkVxzDHHFL/+9a+Lurq64re//W3xla98pZg9e7brrQX7qH1zvbVc77zzTtG7d+9i1qxZRVEUxfz584s+ffoU8+fPd71VkBYfG3PmzCkGDBjQ6Njo0aOLBx98sEwz2recccYZxcyZM3c6fsIJJxRz5swp3Z4+fXpx5plnFkVRFNdee20xadKk0rn169cXvXr1KpYsWVIsXry46NWrV7Fu3brS+e985zvFddddVxTF9n8BPPDAA6Vzzz333E77z85+/OMfF6eddlpx++23N/pNazn26YEHHig9R71BgwYVc+fObcYVtw672rf777+/GDt2bJOPsW/l9dJLLxWXXXZZo2MTJkwofvzjH7veWrCP2jfXW8tW/4fLdXV1xdy5c4va2tpiyZIlrrcK0uK/RrVo0aLo3r17o2PdunWLN954o0wz2nfU1dXFggUL4pe//GUMGDAghg4dGlOnTo33338/3n333ejRo0fpvkcccURpT3bcs5qamjjkkEPijTfeiDfffDMOOeSQ6NixY+l8t27dYsGCBU0+tlu3bvFf//VfsWbNmuTVVrbRo0fHww8/HL179y4dW7t2bVn2qalrtuHz8j+a2reIiNdeey1Wr14dI0aMiK997WsxceLEeOeddyLCvpXbcccdF//0T/9Uur1mzZr493//9/jyl7/semvBPmrfXG8t2/777x8bNmyIPn36xNixY+Nb3/pWHHjgga63CtLiY2P9+vXRoUOHRsdqampiw4YNZZrRvmP16tXRu3fvOPXUU+Ppp5+OKVOmxP333x/33HNPRESjfampqYmNGzdGxPY9q6mpaTRWhw4dYsOGDbFu3bom93NXj62/b/15mvb5z39+p2Pr16+PiL2/Tx81Lo01tW8R21/rvn37xs9+9rN44oknokOHDjFhwoSIsG8tyQcffBDjx4+PP/uzP4tevXpFhOutEjTctxNPPNH1VgHat28f//Ef/xEPPfRQzJgxI6ZNmxYRrrdKUV3uCexOx44dY9OmTY2ObdiwoVGRkuOggw6Ke++9t3T7qKOOinPOOSdmz54dEdFoXxruScOLtt7GjRtjv/32i6IoPnI/d3xs/a/t98dX/y/Evb1PuxrXHu65v//7v290+9JLL43+/fvHypUr7VsLsWTJkrj44oujR48ecc0115ReO9dby7bjvrVp08b1VgHatGkTn/nMZ6JPnz5xxhlnxPz58yPC9VYpWvwnG927d48lS5Y0OrZ48eJGH52RY+HChTFlypRGx7Zs2RLt27ePgw46KBYvXlw6vmTJktKe9OjRo9GebdiwIVauXBndu3eP7t27x4oVKxpdrA33c8fHLl68OA466KA44IADUtbYmnXq1Kks+7TjuR0fy+7dcMMN8eabb5Zub9myJSK2/+mefSu/l156Kc4444w46aSTYsqUKdG+fXvXWwVoat8iXG8t2YsvvhijRo1qdGzz5s1xwAEHuN4qSIuPja9+9atRFEXcfffdsWXLlnjsscdiwYIFMXTo0HJPrdU74IAD4q677orp06fHtm3bYv78+XHPPffEqFGjYuTIkXHzzTfH6tWrY/ny5XHHHXfEyJEjIyLi5JNPjqeeeipeeOGF2Lx5c1x77bVx1FFHxRFHHBHdunWLP/3TP43rr78+Nm/eHPPmzYunnnoqTj755IiIGDlyZNx5553x9ttvx+rVq+PGG2+MU045pZwvQ0Urxz4NHTo0Xn/99Xj88cdjy5Ytcccdd0SbNm2iX79+ZXsdKs2CBQvi6quvjrVr18batWvjhz/8YQwePDgOPPBA+1Zmy5Yti4suuigmTpwY3/nOd6Kqqqp0zvXWcn3UvrneWq6jjjoq3nnnnbjrrruirq4uXn755ZgxY0b81V/9leutkpTxh9P32IIFC4rRo0cXtbW1xcknn1w8//zz5Z7SPuP5558vTjvttKK2trYYMmRIce+99xZFURQbN24sLr/88qJ///7FV7/61eK6664rtm3bVnrcE088UQwbNqyora0tzjvvvOLtt98unVuxYkXx7W9/u+jbt29x4oknFo899ljpXF1dXXHDDTcUAwYMKI477rji+9//frFp06a9t+AKN2/evEZ/q1G59unFF18sTjnllKK2trb45je/Wfz+979PXnll23HfVq9eXUyaNKno169fceyxxxaTJk0q1qxZUzpv38rnyiuvLP7kT/6kqK2tbfTPv/zLv7jeWrCP2jfXW8s2f/78YvTo0UXfvn2L4cOHF08++WRRFP77VkmqiqIoyh08AABA69Piv0YFAABUJrEBAACkEBsAAEAKsQEAAKQQGwAAQAqxAQAApBAbAPuwb3zjG3HvvfdGRMSYMWNi8uTJZZ4RAK1JdbknAED5PPTQQ1FTU1PuaQDQSokNgH3YgQceWO4pANCK+RoVQCvSs2fPeOyxx2LkyJHRp0+fOOuss2L58uXxgx/8IPr27RuDBg2KX/ziF6X7N/wa1Y6effbZOOWUU+Loo4+O4cOHx4wZMxqdv/vuu2PYsGHRu3fv6NevX3z3u9+NdevWlc4//vjjMWzYsDj66KPjoosuin/+53+OSy+9dI/HB6DyiQ2AVubaa6+N733vezF9+vRYuXJljBo1Kvbff/946KGH4s///M/jH//xHxtFQVMWLlwYEydOjNGjR8fMmTNjwoQJMXny5HjsscciImLmzJlx4403xqWXXhpPPvlkXHXVVfHUU0/Fgw8+GBERL7/8cnz3u9+Ns88+O37xi19Ez549G0XN7sYHoHXwNSqAVubss8+O448/PiIiBg8eHLNmzYpJkyZFVVVV/PVf/3Xcc889sXz58ujZs+cux7j99ttjxIgRcdZZZ0VExOGHHx7Lli2LO+64I4YPHx5//Md/HFdffXUMGTIkIiIOPfTQ6NevXyxatCgiIn7+85/HkCFD4rzzzouIiEmTJsW8efP2eHwAWgexAdDKdO3atfTrmpqaOPTQQ6OqqioiItq3bx8REZs3b/7IMRYuXBhvvPFGo08atm7dGtXV2/+z0a9fv3j11Vfj+uuvjyVLlsTChQtjyZIlceqpp0ZExIIFC2LEiBGNxqytrY21a9fu0fgAtA7+rQ7Qyuz4G/Y2bT7+N2br6upizJgxMXr06CbPP/zww3H55ZfHqFGjYuDAgXHxxRfHlClTGs1h27Ztn3h8AFoHP7MBwE66d+8eS5cuja5du5b+efHFF+O+++6LiO1fk/r2t78dl19+eXzzm9+Mnj17xtKlS6MoioiIOPLII+N3v/tdozFfffXVPR4fgNZBbACwk7Fjx8asWbPi1ltvjaVLl8aTTz4ZV155ZXTp0iUiIjp37hwvvPBCLFq0KBYuXBjf//73Y9GiRaWvZ5177rnxzDPPxLRp02LJkiVx8803x8svv1z6OtfuxgegdRAbAOykd+/eMWXKlHj88cdj+PDhcfXVV8e4cePiwgsvjIiIf/iHf4iqqqo4/fTT4/zzz4/NmzfHRRddFK+99lpERPTp0yeuvPLKuPvuu2PkyJExf/78OPHEE6Ndu3Z7ND4ArUNVUf+ZNwA0k9/+9rfRsWPHOPLII0vHxo0bF0cffXRccsklZZwZAHuTTzYAaHavvPJKXHDBBfHSSy/F22+/HQ8++GDMmzcvhg0bVu6pAbAX+WQDgGZXV1cXP/rRj2LmzJmxdu3a6N69e/zt3/5tDB48uNxTA2AvEhsAAEAKX6MCAABSiA0AACCF2AAAAFKIDQAAIIXYAAAAUogNAAAgxf8HoNdfp6Rh1cMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1008x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data=df, x='mileage')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "88853b8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12696"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Detect all outliers\n",
    "\n",
    "from datasist.structdata import detect_outliers\n",
    "out_idx = detect_outliers(df, 0, ['price', 'mileage','year','engineSize','mpg'])\n",
    "\n",
    "len(out_idx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "a1a996f2",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>year</th>\n",
       "      <th>price</th>\n",
       "      <th>transmission</th>\n",
       "      <th>mileage</th>\n",
       "      <th>fuelType</th>\n",
       "      <th>tax</th>\n",
       "      <th>mpg</th>\n",
       "      <th>engineSize</th>\n",
       "      <th>company name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A1</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>12500.0</td>\n",
       "      <td>Manual</td>\n",
       "      <td>15735.0</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>150.0</td>\n",
       "      <td>55.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>audi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A6</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>16500.0</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>36203.0</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>20.0</td>\n",
       "      <td>64.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>audi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A1</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>11000.0</td>\n",
       "      <td>Manual</td>\n",
       "      <td>29946.0</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>30.0</td>\n",
       "      <td>55.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>audi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A4</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>16800.0</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>25952.0</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>145.0</td>\n",
       "      <td>67.3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>audi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A3</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>17300.0</td>\n",
       "      <td>Manual</td>\n",
       "      <td>1998.0</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>145.0</td>\n",
       "      <td>49.6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>audi</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  model    year    price transmission  mileage fuelType    tax   mpg  \\\n",
       "0    A1  2017.0  12500.0       Manual  15735.0   Petrol  150.0  55.4   \n",
       "1    A6  2016.0  16500.0    Automatic  36203.0   Diesel   20.0  64.2   \n",
       "2    A1  2016.0  11000.0       Manual  29946.0   Petrol   30.0  55.4   \n",
       "3    A4  2017.0  16800.0    Automatic  25952.0   Diesel  145.0  67.3   \n",
       "4    A3  2019.0  17300.0       Manual   1998.0   Petrol  145.0  49.6   \n",
       "\n",
       "   engineSize company name  \n",
       "0         1.4         audi  \n",
       "1         2.0         audi  \n",
       "2         1.4         audi  \n",
       "3         2.0         audi  \n",
       "4         1.0         audi  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop outliers\n",
    "df.drop(index = out_idx, inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "6fcef09d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([' A1', ' A6', ' A4', ' A3', ' Q3', ' Q5', ' A5', ' S4', ' Q2',\n",
       "       ' A7', ' TT', ' Q7', ' A8', ' RS3', ' SQ5', ' S3', ' S5',\n",
       "       ' 5 Series', ' 6 Series', ' 1 Series', ' 7 Series', ' 2 Series',\n",
       "       ' 4 Series', ' X3', ' 3 Series', ' X4', ' X1', ' M4', ' X2', ' X6',\n",
       "       ' X5', ' Z4', ' M2', ' M3', ' C Class', ' Focus', ' Fiesta',\n",
       "       ' Puma', ' Kuga', ' EcoSport', ' C-MAX', ' Mondeo', ' Ka+',\n",
       "       ' Tourneo Custom', ' S-MAX', ' B-MAX', ' Edge', ' Tourneo Connect',\n",
       "       ' Grand C-MAX', ' KA', ' Galaxy', ' Grand Tourneo Connect',\n",
       "       ' Mustang', ' Transit Tourneo', ' I20', ' Tucson', ' I10', ' IX35',\n",
       "       ' I30', ' I40', ' Kona', ' Veloster', ' I800', ' IX20',\n",
       "       ' Santa Fe', ' Ioniq', ' S Class', ' GLE Class', ' GLA Class',\n",
       "       ' A Class', ' B Class', ' E Class', ' GL Class', ' SL CLASS',\n",
       "       ' CLS Class', ' GLC Class', ' SLK', ' V Class', ' CLA Class',\n",
       "       ' CL Class', ' M Class', ' GLB Class', ' X-CLASS', ' GLS Class',\n",
       "       '180', '220', '200', ' Octavia', ' Citigo', ' Yeti Outdoor',\n",
       "       ' Superb', ' Kodiaq', ' Rapid', ' Karoq', ' Fabia', ' Yeti',\n",
       "       ' Scala', ' Roomster', ' Kamiq', ' GT86', ' Corolla', ' RAV4',\n",
       "       ' Yaris', ' Auris', ' Aygo', ' C-HR', ' Prius', ' Avensis',\n",
       "       ' Verso', ' Hilux', ' PROACE VERSO', ' Land Cruiser', ' Camry',\n",
       "       ' Verso-S', ' IQ', ' Urban Cruiser', ' Corsa', ' Astra', ' Viva',\n",
       "       ' Mokka', ' Mokka X', ' Crossland X', ' Zafira', ' Meriva',\n",
       "       ' Zafira Tourer', ' Adam', ' Grandland X', ' Antara', ' Insignia',\n",
       "       ' GTC', ' Combo Life', ' Vivaro', ' Cascada', ' Kadjar', ' Agila',\n",
       "       ' T-Roc', ' Golf', ' Passat', ' T-Cross', ' Polo', ' Tiguan',\n",
       "       ' Sharan', ' Up', ' Scirocco', ' Beetle', ' Caddy Maxi Life',\n",
       "       ' Caravelle', ' Touareg', ' Arteon', ' Touran', ' Golf SV',\n",
       "       ' Amarok', ' Tiguan Allspace', ' Shuttle', ' Jetta', ' CC',\n",
       "       ' Caddy Life', ' Caddy', ' Caddy Maxi', ' Eos'], dtype=object)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#there is a space in front of each model name.\n",
    "df['model'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "7dcadd80",
   "metadata": {},
   "outputs": [],
   "source": [
    "#cleaning the model names\n",
    "df['model'] = [i.strip()   for i in df['model']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "ba1904ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['A1', 'A6', 'A4', 'A3', 'Q3', 'Q5', 'A5', 'S4', 'Q2', 'A7'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['model'].unique()[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "e2156060",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Manual', 'Automatic', 'Semi-Auto', 'Other'], dtype=object)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['transmission'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "ff327a49",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Petrol', 'Diesel', 'Hybrid', 'Other', 'Electric'], dtype=object)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['fuelType'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "3d8af93c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['audi', 'bmw', 'cclass', 'focus', 'ford', 'hyundi', 'merc',\n",
       "       'skoda', 'toyota', 'vauxhall', 'vw'], dtype=object)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['company name'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "d9961b90",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2017., 2016., 2019., 2015., 2014., 2018., 2013., 2020., 2012.])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['year'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "49197f4b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# converting year column to the suitable data type 'integer'\n",
    "df['year'] = df['year'].convert_dtypes() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "0f8d53ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Dtype()"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['year'].dtype"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eace7f09",
   "metadata": {},
   "source": [
    "## Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "b3dd3510",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x720 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows=3, ncols=2)\n",
    "\n",
    "sns.histplot(data=df, x='year',ax=axes[0,0])\n",
    "sns.histplot(data=df, x='tax', ax=axes[0,1])\n",
    "sns.histplot(data=df, x='mpg', ax=axes[1,0])\n",
    "sns.histplot(data=df, x='engineSize', ax=axes[1,1])\n",
    "sns.histplot(data=df,  x='price', ax=axes[2,0])\n",
    "sns.histplot(data=df,  x='mileage', ax=axes[2,1])\n",
    "\n",
    "plt.tight_layout()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "18d0f311",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, '2012'),\n",
       " Text(1, 0, '2013'),\n",
       " Text(2, 0, '2014'),\n",
       " Text(3, 0, '2015'),\n",
       " Text(4, 0, '2016'),\n",
       " Text(5, 0, '2017'),\n",
       " Text(6, 0, '2018'),\n",
       " Text(7, 0, '2019'),\n",
       " Text(8, 0, '2020')]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax=sns.countplot(data=df, x='year')\n",
    "ax.set_xticklabels(ax.get_xticklabels(),rotation = 45)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "dc968d35",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='company name', ylabel='count'>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data=df, x='company name')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "7d8def58",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='fuelType', ylabel='count'>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data=df, x='fuelType')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "6c611a2f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='company name', ylabel='count'>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data=df, x='company name', hue='fuelType')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "6122065f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='company name', ylabel='count'>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "HEO=['Other','Hybrid', 'Electric']\n",
    "sns.countplot(data=df.loc[df['fuelType'].isin(HEO)], x='company name', hue='fuelType')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "0af39b9a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='company name', ylabel='count'>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "EO=['Other', 'Electric']\n",
    "sns.countplot(data=df.loc[df['fuelType'].isin(EO)], x='company name', hue='fuelType')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "897a6fb8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>year</th>\n",
       "      <th>price</th>\n",
       "      <th>transmission</th>\n",
       "      <th>mileage</th>\n",
       "      <th>fuelType</th>\n",
       "      <th>tax</th>\n",
       "      <th>mpg</th>\n",
       "      <th>engineSize</th>\n",
       "      <th>company name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>37187</th>\n",
       "      <td>Mondeo</td>\n",
       "      <td>2016</td>\n",
       "      <td>15975.0</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>9396.0</td>\n",
       "      <td>Electric</td>\n",
       "      <td>0.0</td>\n",
       "      <td>67.3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>ford</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42761</th>\n",
       "      <td>Mondeo</td>\n",
       "      <td>2016</td>\n",
       "      <td>15500.0</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>24531.0</td>\n",
       "      <td>Electric</td>\n",
       "      <td>0.0</td>\n",
       "      <td>67.3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>ford</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        model  year    price transmission  mileage  fuelType  tax   mpg  \\\n",
       "37187  Mondeo  2016  15975.0    Automatic   9396.0  Electric  0.0  67.3   \n",
       "42761  Mondeo  2016  15500.0    Automatic  24531.0  Electric  0.0  67.3   \n",
       "\n",
       "       engineSize company name  \n",
       "37187         2.0         ford  \n",
       "42761         2.0         ford  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['fuelType']=='Electric'] #only 2 rows are electric so its outlier so i can remove it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "7f8131b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(df[df['fuelType']=='Electric'].index ,inplace =True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "aea66344",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(191, 10)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['fuelType']=='Other'].shape #only 190 rows are electric so its outlier so i can remove it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "705b3bc5",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(df[df['fuelType']=='Other'].index ,inplace =True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "ce07585e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='company name', ylabel='mpg'>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(data=df, x='company name', y='mpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "f46c743d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='model', ylabel='mpg'>"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(data=df, x='model', y='mpg')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "5ec998f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='transmission', ylabel='count'>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data=df, x='transmission')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "c3f5aed3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([25264, 53528, 66389, 72872, 75471, 86455, 87070, 87173, 118063], dtype='int64')"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['transmission']== 'Other'].index # only 11 rows are transimition type Other, so its an outlier and i can delete it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "7a8d43ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(105260, 10)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "d494b24f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(105251, 10)"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(df[df['transmission']== 'Other'].index ,inplace =True)\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "a4da1e10",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "df.to_csv('All cars data.csv', index=False )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "f3018257",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 3, 2], dtype=int64)"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# renaming transmission values\n",
    "\n",
    "transmition_map = {'Manual':1, 'Semi-Auto':2, 'Automatic':3}\n",
    "\n",
    "df['transmission'] = df['transmission'].map(transmition_map)\n",
    "df.transmission.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "77c9d8b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.corr(), annot=True)\n",
    "\n",
    "#The most effictive features are year then enginesize then transmission  then mileage."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c565af6f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  },
  "toc": {
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   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": true,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}